Various systems and methods are available for processing reads in which one or more differences between a reference data set and a client data set may be identified as sparse indicators. Different types of sparse indicators vary in the degree to which they can be accurately identified. For example, sparse indicators corresponding to duplications and deletions may be particularly difficult to identify due to the differences between the reference data set and the client data set being spread over a wide range of identifiers. In contrast, other types of sparse indicators occur over a narrow range of identifiers or at a single identifier which may be more accurately identified. Conventional approaches to identify duplications and deletions that rely only on a direct comparison between the reference data set and the client data set are inaccurate and produce unsatisfactory results. Accordingly, new systems, methods, and other techniques for accurately identifying duplications and deletions are needed.